Mixed effects models but not t-tests or linear regression detect progression of apathy in Parkinson's disease over seven years in a cohort: a comparative analysis.

INTRODUCTION: While there is an interest in defining longitudinal change in people with chronic illness like Parkinson's disease (PD), statistical analysis of longitudinal data is not straightforward for clinical researchers. Here, we aim to demonstrate how the choice of statistical method may influence research outcomes, (e.g., progression in apathy), specifically the size of longitudinal effect estimates, in a cohort. METHODS: In this retrospective longitudinal analysis of 802 people with typical Parkinson's disease in the Luxembourg Parkinson's study, we compared the mean apathy scores at visit 1 and visit 8 by means of the paired two-sided t-test. Additionally, we analysed the relationship between the visit numbers and the apathy score using linear regression and longitudinal two-level mixed effects models. RESULTS: Mixed effects models were the only method able to detect progression of apathy over time. While the effects estimated for the group comparison and the linear regression were smaller with high p-values (+ 1.016/ 7 years, p = 0.107, -0.056/ 7 years, p = 0.897, respectively), effect estimates for the mixed effects models were positive with a very small p-value, indicating a significant increase in apathy symptoms by + 2.345/ 7 years (p < 0.001). CONCLUSION: The inappropriate use of paired t-tests and linear regression to analyse longitudinal data can lead to underpowered analyses and an underestimation of longitudinal change. While mixed effects models are not without limitations and need to be altered to model the time sequence between the exposure and the outcome, they are worth considering for longitudinal data analyses. In case this is not possible, limitations of the analytical approach need to be discussed and taken into account in the interpretation.

The artificial intelligence advantage: Supercharging exploratory data analysis.

Explorative data analysis (EDA) is a critical step in scientific projects, aiming to uncover valuable insights and patterns within data. Traditionally, EDA involves manual inspection, visualization, and various statistical methods. The advent of artificial intelligence (AI) and machine learning (ML) has the potential to improve EDA, offering more sophisticated approaches that enhance its efficacy. This review explores how AI and ML algorithms can improve feature engineering and selection during EDA, leading to more robust predictive models and data-driven decisions. Tree-based models, regularized regression, and clustering algorithms were identified as key techniques. These methods automate feature importance ranking, handle complex interactions, perform feature selection, reveal hidden groupings, and detect anomalies. Real-world applications include risk prediction in total hip arthroplasty and subgroup identification in scoliosis patients. Recent advances in explainable AI and EDA automation show potential for further improvement. The integration of AI and ML into EDA accelerates tasks and uncovers sophisticated insights. However, effective utilization requires a deep understanding of the algorithms, their assumptions, and limitations, along with domain knowledge for proper interpretation. As data continues to grow, AI will play an increasingly pivotal role in EDA when combined with human expertise, driving more informed, data-driven decision-making across various scientific domains. Level of Evidence: Level V - Expert opinion.

Sine-skewed toroidal distributions and their application in protein bioinformatics.

In the bioinformatics field, there has been a growing interest in modeling dihedral angles of amino acids by viewing them as data on the torus. This has motivated, over the past years, new proposals of distributions on the torus. The main drawback of most of these models is that the related densities are (pointwise) symmetric, despite the fact that the data usually present asymmetric patterns. This motivates the need to find a new way of constructing asymmetric toroidal distributions starting from a symmetric distribution. We tackle this problem in this article by introducing the sine-skewed toroidal distributions. The general properties of the new models are derived. Based on the initial symmetric model, explicit expressions for the shape and dependence measures are obtained, a simple algorithm for generating random numbers is provided, and asymptotic results for the maximum likelihood estimators are established. An important feature of our construction is that no extra normalizing constant needs to be calculated, leading to more flexible distributions without increasing the complexity of the models. The benefit of employing these new sine-skewed toroidal distributions is shown on the basis of protein data, where, in general, the new models outperform their symmetric antecedents.

Bilateral simultaneous hip arthroplasty shows comparable early outcome and complication rate as staged bilateral hip arthroplasty for patients scored ASA 1-3 if performed by a high-volume surgeon.

PURPOSE: The aim of this study was to compare early outcomes after simultaneous and staged hip arthroplasty (THA) in patients with bilateral symptomatic pathology. METHODS: We conducted a retrospective cohort study including all patients scheduled for primary THA for bilateral hip osteoarthritis (OA, n = 290). Patients either received simultaneous (n = 152, 52.4%) or staged (n = 138, 47.6%) bilateral THA based on individual preference. All operations (n = 428) were performed by one single, high-volume surgeon. Demographic data (e.g., age, ASA score) as well as perioperative parameters (haemoglobin drop (Hb), red blood cell transfusion, length of stay (LOS), operation time, six week complication rate and achievement of inpatient rehabilitation key points) were evaluated. RESULTS: Patients in the simultaneous bilateral THA group were younger (62.8 ± 8.9 vs. 65 ± 9.7 years, p = 0.022) and had lower ASA scores (1.8, (34.2% ASA 1, 55.3% ASA 2, 37.2% ASA 3) vs. 2.0 (18.8% ASA 1, 61.6% ASA 2, 19.6% ASA 3)) than the staged group. While the average LOS was 7.1 ± 1.7 days for simultaneous bilateral THA, the combined LOS for the staged group was 12.9 ± 2.4 days (p < 0.001). The cumulative operation time in the simultaneous bilateral THA group was 61.1 ± 11.5 min and 57.6 ± 7.3 min in the staged group (p < 0.015). Cumulative Hb loss was significantly higher in the staged group (2.1 ± 7.2 g/dl vs. 3.7 ± 1.3 g/dl, p < 0.001). No significant differences were found concerning the complication rate or early inpatient rehabilitation. CONCLUSION: Simultaneous bilateral hip arthroplasty in patients with symptomatic bilateral hip osteoarthritis is as safe and successful as a staged procedure if performed by a high-volume surgeon.

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons.

The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care.Level of evidence VI.

Machine learning and conventional statistics: making sense of the differences.

The application of machine learning (ML) to the field of orthopaedic surgery is rapidly increasing, but many surgeons remain unfamiliar with the nuances of this novel technique. With this editorial, we address a fundamental topic-the differences between ML techniques and traditional statistics. By doing so, we aim to further familiarize the reader with the new opportunities available thanks to the ML approach.

Modelling COVID-19 dynamics and potential for herd immunity by vaccination in Austria, Luxembourg and Sweden.

Against the COVID-19 pandemic, non-pharmaceutical interventions have been widely applied and vaccinations have taken off. The upcoming question is how the interplay between vaccinations and social measures will shape infections and hospitalizations. Hence, we extend the Susceptible-Exposed-Infectious-Removed (SEIR) model including these elements. We calibrate it to data of Luxembourg, Austria and Sweden until 15 December 2020. Sweden results having the highest fraction of undetected, Luxembourg of infected and all three being far from herd immunity in December. We quantify the level of social interaction, showing that a level around 1/3 of before the pandemic was still required in December to keep the effective reproduction number Refft below 1, for all three countries. Aiming to vaccinate the whole population within 1 year at constant rate would require on average 1,700 fully vaccinated people/day in Luxembourg, 24,000 in Austria and 28,000 in Sweden, and could lead to herd immunity only by mid summer. Herd immunity might not be reached in 2021 if too slow vaccines rollout speeds are employed. The model thus estimates which vaccination rates are too low to allow reaching herd immunity in 2021, depending on social interactions. Vaccination will considerably, but not immediately, help to curb the infection; thus limiting social interactions remains crucial for the months to come.

Regression Models for Cylindrical Data in Psychology.

Cylindrical data are multivariate data which consist of a directional, in this paper circular, and a linear component. Examples of cylindrical data in psychology include human navigation (direction and distance of movement), eye-tracking research (direction and length of saccades) and data from an interpersonal circumplex (location and intensity on the IPC). In this paper we adapt four models for cylindrical data to include a regression of the circular and linear component onto a set of covariates. Subsequently, we illustrate how to fit these models and interpret their results on a dataset on the interpersonal behavior of teachers.

Mutual point-winning probabilities (MPW): a new performance measure for table tennis.

We propose a new performance measure for table tennis players: the mutual point-winning probabilities (MPW) as server and receiver. The MPWs quantify a player's chances to win a point against a given opponent, and hence nicely complement the classical match statistics history between two players. These new quantities are based on a Bradley-Terry-type statistical model taking into account the importance of individual points, since a rally at 8-2 in the first set is less crucial than a rally at the score of 9-9 in the final set. The MPWs hence reveal a player's strength on his/her service against a given opponent as well as his/her capacity of scoring crucial points. We estimate the MPWs by means of maximum likelihood estimation and show via a Monte Carlo simulation study that our estimation procedure works well. In order to illustrate the MPWs' versatile use, we have organized two round-robin tournaments of ten respectively eleven table tennis players from the Belgian table tennis federation. We compare the classical final ranking to the ranking based on MPWs, and we highlight how the MPWs shed new light on strengths and weaknesses of the players.

A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.

Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI-based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self-supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI-driven orthopaedic research. Level of Evidence: Level IV.

A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?

Artificial intelligence's (AI) accelerating progress demands rigorous evaluation standards to ensure safe, effective integration into healthcare's high-stakes decisions. As AI increasingly enables prediction, analysis and judgement capabilities relevant to medicine, proper evaluation and interpretation are indispensable. Erroneous AI could endanger patients; thus, developing, validating and deploying medical AI demands adhering to strict, transparent standards centred on safety, ethics and responsible oversight. Core considerations include assessing performance on diverse real-world data, collaborating with domain experts, confirming model reliability and limitations, and advancing interpretability. Thoughtful selection of evaluation metrics suited to the clinical context along with testing on diverse data sets representing different populations improves generalisability. Partnering software engineers, data scientists and medical practitioners ground assessment in real needs. Journals must uphold reporting standards matching AI's societal impacts. With rigorous, holistic evaluation frameworks, AI can progress towards expanding healthcare access and quality. Level of Evidence: Level V.

The Probabilistic Final Standing Calculator: a fair stochastic tool to handle abruptly stopped football seasons.

The COVID-19 pandemic has left its marks in the sports world, forcing the full stop of all sports-related activities in the first half of 2020. Football leagues were suddenly stopped, and each country was hesitating between a relaunch of the competition and a premature ending. Some opted for the latter option and took as the final standing of the season the ranking from the moment the competition got interrupted. This decision has been perceived as unfair, especially by those teams who had remaining matches against easier opponents. In this paper, we introduce a tool to calculate in a fairer way the final standings of domestic leagues that have to stop prematurely: our Probabilistic Final Standing Calculator (PFSC). It is based on a stochastic model taking into account the results of the matches played and simulating the remaining matches, yielding the probabilities for the various possible final rankings. We have compared our PFSC with state-of-the-art prediction models, using previous seasons which we pretend to stop at different points in time. We illustrate our PFSC by showing how a probabilistic ranking of the French Ligue 1 in the stopped 2019-2020 season could have led to alternative, potentially fairer, decisions on the final standing. Supplementary Information: The online version contains supplementary material available at 10.1007/s10182-021-00416-6.

TailCoR: A new and simple metric for tail correlations that disentangles the linear and nonlinear dependencies that cause extreme co-movements.

Economic and financial crises are characterised by unusually large events. These tail events co-move because of linear and/or nonlinear dependencies. We introduce TailCoR, a metric that combines (and disentangles) these linear and non-linear dependencies. TailCoR between two variables is based on the tail inter quantile range of a simple projection. It is dimension-free, and, unlike competing metrics, it performs well in small samples and no optimisations are needed. Indeed, TailCoR requires a few lines of coding and it is very fast. A Monte Carlo analysis confirms the goodness of the metric, which is illustrated on a sample of 21 daily financial market indexes across the globe and for 20 years. The estimated TailCoRs are in line with the financial and economic events, such as the 2008 great financial crisis and the 2020 pandemic.

Relationship between a daily injury risk estimation feedback (I-REF) based on machine learning techniques and actual injury risk in athletics (track and field): protocol for a prospective cohort study over an athletics season.

INTRODUCTION: Two-thirds of athletes (65%) have at least one injury complaint leading to participation restriction (ICPR) in athletics (track and field) during one season. The emerging practice of medicine and public health supported by electronic processes and communication in sports medicine represents an opportunity for developing new injury risk reduction strategies. Modelling and predicting the risk of injury in real-time through artificial intelligence using machine learning techniques might represent an innovative injury risk reduction strategy. Thus, the primary aim of this study will be to analyse the relationship between the level of Injury Risk Estimation Feedback (I-REF) use (average score of athletes' self-declared level of I-REF consideration for their athletics activity) and the ICPR burden during an athletics season. METHOD AND ANALYSIS: We will conduct a prospective cohort study, called Injury Prediction with Artificial Intelligence (IPredict-AI), over one 38-week athletics season (from September 2022 to July 2023) involving competitive athletics athletes licensed with the French Federation of Athletics. All athletes will be asked to complete daily questionnaires on their athletics activity, their psychological state, their sleep, the level of I-REF use and any ICPR. I-REF will present a daily estimation of the ICPR risk ranging from 0% (no risk for injury) to 100% (maximal risk for injury) for the following day. All athletes will be free to see I-REF and to adapt their athletics activity according to I-REF. The primary outcome will be the ICPR burden over the follow-up (over an athletics season), defined as the number of days lost from training and/or competition due to ICPR per 1000 hours of athletics activity. The relationship between ICPR burden and the level of I-REF use will be explored by using linear regression models. ETHICS AND DISSEMINATION: This prospective cohort study was reviewed and approved by the Saint-Etienne University Hospital Ethical Committee (Institutional Review Board: IORG0007394, IRBN1062022/CHUSTE). Results of the study will be disseminated in peer-reviewed journals and in international scientific congresses, as well as to the included participants.

A practical guide to the implementation of AI in orthopaedic research - part 1: opportunities in clinical application and overcoming existing challenges.

Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application. Level of evidence IV.

How injury registration and preseason assessment are being delivered: An international survey of sports physical therapists.

OBJECTIVE: To identify the role of sports physical therapists (PT) in the organization of injury registration and preseason assessment, applied in athletic organizations and sports teams of different gender and level world-wide. DESIGN: cross-sectional study. SETTING: LimeSurvey platform. PARTICIPANTS: Sports PTs working with athletes invited through International Federation of Sports Physical Therapy. MAIN OUTCOME MEASURES: injury registration and athlete's screening. RESULTS: 414 sports PTs participated in this international survey (mean age of 37.66 (SD = 9.38) years). 340 participants indicated that the PT as the responsible for injury registration. Barriers to properly register injury throughout the season were indicated by 157 sports PT and 86 (54.77%) indicated a lack of time on their routine as the main factor. 93 participants (30.09%) indicated that they customize the prevention program based on the preseason assessment. Sports PTs who reported not performing a preseason assessment (92 participants - 22.22%) mainly indicated this to be consequence of lack of structure in the organization (44 participants - 47.82%). CONCLUSION: The majority of the sports PTs participate on injury registration and perform preseason assessment in athletes. However, lack of time in their routine and structure in the organization were recognized as the most important barriers to organize these properly.

How injury prevention programs are being structured and implemented worldwide: An international survey of sports physical therapists.

OBJECTIVE: To identify the role of sports physical therapists (PT) in the injury prevention process and to compare the structure of preventive programs and associated (organization) policies applied in athletic organizations and sports teams of varying gender and level world-wide. DESIGN: cross-sectional study. SETTING: LimeSurvey platform. PARTICIPANTS: Sports PT working with athletes invited through the International Federation of Sports Physical Therapy. MAIN OUTCOME MEASURES: Sports injury prevention program (IPP) structure and implementation. RESULTS: 414 participants fully participate in this survey study. Athlete's injury history (68.84%), the most common injuries within the sport modality (67.87%) and athlete's preseason screening results (64.01%) were most frequently used to customize IPPs. Warm-up (70.04%) and individually PT-guided exercise-therapy (70.04%) were the preferred methods to organize the prevention routine. The main barrier for IPP implementation was lack of time within the athlete's weekly training schedule (66.66%). The majority of the participants (72.84%) reported to evaluate the perception of IPP's effect by comparing current and preceding seasons' injury occurrences. CONCLUSION: These survey results are the first identifying contemporary sports injury prevention organization and implementation policies on an international level. This information might support the sports PT community in improving and standardizing IPP (implementation) strategies worldwide.

The Safety of Bilateral Simultaneous Hip and Knee Arthroplasty versus Staged Arthroplasty in a High-Volume Center Comparing Blood Loss, Peri- and Postoperative Complications, and Early Functional Outcome.

PURPOSE: In recent years, there has been increasing interest in the use of simultaneous hip and knee arthroplasty compared to staged procedures in patients with bilateral pathology. The aim of this study was to compare simultaneous and staged hip and knee arthroplasty in patients with bilateral pathology by assessing the transfusion rate, postoperative hemoglobin drop, length of stay (LOS), in-hospital complications, 30-day readmissions and early functional outcome. METHODS: We conducted a retrospective cohort study that included all patients who were undergoing primary TKA, THA and UKA by a single surgeon in a high-volume arthroplasty center between 2015 and 2020 as simultaneous or staged procedures. Staged bilateral arthroplasties were performed within 12 months and were stratified by the time between procedures. Data were acquired through the electronic files at the Orthopädische Chirurgie München (OCM). For functional outcome, the ability of the patients to walk independently on the ward was compared with the ability to walk a set of stairs alone, which was recorded daily by the attending physiotherapist. RESULTS: In total n = 305 patients were assessed for eligibility and included in this study. One hundred and forty-five patients were allocated to the staged arthroplasty group. This group was subdivided into a hip and a knee group, whereas the knee group was split into TKA and UKA. The second staged procedure was performed within 12 months of the first procedure. One hundred and sixty patients were allocated to the simultaneous arthroplasty group. This group was also subdivided into a hip and knee group, whereas the knee group was split again into a TKA and UKA group. No statistical difference was found between the two groups regarding demographic data. Primary outcome measurements: There was no significant difference in the transfusion rate or complication rate. Secondarily, no statistically significant difference was found between the postoperative hemoglobin drop and the functional outcome, or in the length of stay (LOS) between both groups. Walking the stairs showed a significant difference in the knee group. CONCLUSIONS: There were no significant differences observed in the transfusion rate in-hospital complications, or readmission rate between both groups. The early functional outcome showed no significant difference in mobility for all groups. Simultaneous arthroplasty for knee or hip is as safe as a staged procedure, with no higher risk for the patient, in a specialized high-volume center. LEVEL OF EVIDENCE: Level IV.

Coming Together of Bayesian Inference and Skew Spherical Data.

This paper presents Bayesian directional data modeling via the skew-rotationally-symmetric Fisher-von Mises-Langevin (FvML) distribution. The prior distributions for the parameters are a pivotal building block in Bayesian analysis, therefore, the impact of the proposed priors will be quantified using the Wasserstein Impact Measure (WIM) to guide the practitioner in the implementation process. For the computation of the posterior, modifications of Gibbs and slice samplings are applied for generating samples. We demonstrate the applicability of our contribution via synthetic and real data analyses. Our investigation paves the way for Bayesian analysis of skew circular and spherical data.

Motion-Control Shoes Reduce the Risk of Pronation-Related Pathologies in Recreational Runners: A Secondary Analysis of a Randomized Controlled Trial.

OBJECTIVE: To investigate whether motion-control shoes reduce the risk of pronation-related injuries in recreational runners. DESIGN: Secondary analysis of a randomized controlled trial of the effect of shoes on running injuries. METHODS: Three hundred seventy-two recreational runners were randomized to receive either standard neutral or motion-control shoes and were followed up for 6 months regarding running activity and injury. Running injuries that occurred during this period were registered and classified as pronation-related injuries (Achilles tendinopathy, plantar fasciopathy, exercise-related lower-leg pain, and anterior knee pain) or other running-related injuries. With the use of competing risk analysis, the relationship between pronation-related and other running-related injuries and shoe type was evaluated by estimating the cause-specific hazard, controlling for other possible confounders like age, sex, body mass index, previous injury, and sport participation pattern. RESULTS: Twenty-five runners sustained pronation-related running injuries and 68 runners sustained other running-related injuries. Runners wearing the motion-control shoes had a lower risk of pronation-related running injuries compared with runners who wore standard neutral shoes (hazard ratio = 0.41; 95% confidence interval: 0.17, 0.98). There was no effect of shoe type (hazard ratio = 0.68; 95% confidence interval: 0.41, 1.10) on the risk of other running-related injuries. CONCLUSION: Motion-control shoes may reduce the risk of pronation-related running injuries, but did not influence the risk of other running-related injuries. J Orthop Sports Phys Ther 2021;51(3):135-143. Epub 11 Dec 2020. doi:10.2519/jospt.2021.9710.